OpenRaiser/Intern-Atlas huggingface dataset:
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Venue: arXiv cs.CV 2020
Year: 2020

The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw incomplete data. SHARP 2020 is organised as a workshop in conjunction with ECCV 2020. There are two complementary challenges, the first one on 3D human scans, and the second one on generic objects. Challenge 1 is further split into two tracks, focusing, first, on large body and clothing regions, and, second, on fine body details. A novel evaluation metric is proposed to quantify jointly the shape reconstruction, the texture reconstruction and the amount of completed data. Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks. The datasets are released to the scientific community. Moreover, an accompanying custom library of software routines is also released to the scientific community. It allows for processing 3D scans, generating partial data and performing the evaluation. Results of the competition, analysed in comparison to baselines, show the validity of the proposed evaluation metrics, and highlight the challenging aspects of the task and of the datasets. Details on the SHARP 2020 challenge can be found at https://cvi2.uni.lu/sharp2020/.
A Novel Machine Learning Method for Preference Identification
Venue: arXiv cs.AI 2020
Year: 2020

Human preference or taste within any domain is usually a difficult thing to identify or predict with high probability. In the domain of chess problem composition, the same is true. Traditional machine learning approaches tend to focus on the ability of computers to process massive amounts of data and continuously adjust 'weights' within an artificial neural network to better distinguish between say, two groups of objects. Contrasted with chess compositions, there is no clear distinction between what constitutes one and what does not; even less so between a good one and a poor one. We propose a computational method that is able to learn from existing databases of 'liked' and 'disliked' compositions such that a new and unseen collection can be sorted with increased probability of matching a solver's preferences. The method uses a simple 'change factor' relating to the Forsyth-Edwards Notation (FEN) of each composition's starting position, coupled with repeated statistical analysis of sample pairs from both databases. Tested using the author's own collections of computer-generated chess problems, the experimental results showed that the method was able to sort a new and unseen collection of compositions such that, on average, over 70% of the preferred compositions were in the top half of the collection. This saves significant time and energy on the part of solvers as they are likely to find more of what they like sooner. The method may even be applicable to other domains such as image processing because it does not rely on any chess-specific rules but rather just a sufficient and quantifiable 'change' in representation from one object to the next.
Modeling and Simulation of a Point to Point Spherical Articulated Manipulator using Optimal Control
Venue: arXiv cs.RO 2020
Year: 2020

This paper aims to design an optimal stability controller for a point to point trajectory tracking 3 degree of freedom articulated manipulator. The DH convention is used to obtain the forward and inverse kinematics of the manipulator. The manipulator dynamics are formulated using the Lagrange Euler method to obtain a nonlinear system. The complicated nonlinear equations obtained are then linearized in order to implement the optimal LQR. The simulations are performed in MATLAB and Simulink and the optimal controllers performance is tested for various conditions and the results are presented. The results obtained prove the superiority of LQR over conventional PID control.
Interpreting convolutional networks trained on textual data
Venue: arXiv cs.CL 2020
Year: 2020

There have been many advances in the artificial intelligence field due to the emergence of deep learning. In almost all sub-fields, artificial neural networks have reached or exceeded human-level performance. However, most of the models are not interpretable. As a result, it is hard to trust their decisions, especially in life and death scenarios. In recent years, there has been a movement toward creating explainable artificial intelligence, but most work to date has concentrated on image processing models, as it is easier for humans to perceive visual patterns. There has been little work in other fields like natural language processing. In this paper, we train a convolutional model on textual data and analyze the global logic of the model by studying its filter values. In the end, we find the most important words in our corpus to our models logic and remove the rest (95%). New models trained on just the 5% most important words can achieve the same performance as the original model while reducing training time by more than half. Approaches such as this will help us to understand NLP models, explain their decisions according to their word choices, and improve them by finding blind spots and biases.
Optimal Importance Sampling for Federated Learning
Venue: arXiv cs.LG 2020
Year: 2020

Federated learning involves a mixture of centralized and decentralized processing tasks, where a server regularly selects a sample of the agents and these in turn sample their local data to compute stochastic gradients for their learning updates. This process runs continually. The sampling of both agents and data is generally uniform; however, in this work we consider non-uniform sampling. We derive optimal importance sampling strategies for both agent and data selection and show that non-uniform sampling without replacement improves the performance of the original FedAvg algorithm. We run experiments on a regression and classification problem to illustrate the theoretical results.
Learning automata and transducers: a categorical approach
Venue: arXiv cs.FL 2020
Year: 2020

In this paper, we present a categorical approach to learning automata over words, in the sense of the $L^*$-algorithm of Angluin. This yields a new generic $L^*$-like algorithm which can be instantiated for learning deterministic automata, automata weighted over fields, as well as subsequential transducers. The generic nature of our algorithm is obtained by adopting an approach in which automata are simply functors from a particular category representing words to a "computation category". We establish that the sufficient properties for yielding the existence of minimal automata (that were disclosed in a previous paper), in combination with some additional hypotheses relative to termination, ensure the correctness of our generic algorithm.
How angry are your customers? Sentiment analysis of support tickets that escalate
Venue: arXiv cs.SE 2020
Year: 2020

Software support ticket escalations can be an extremely costly burden for software organizations all over the world. Consequently, there exists an interest in researching how to better enable support analysts to handle such escalations. In order to do so, we need to develop tools to reliably predict if, and when, a support ticket becomes a candidate for escalation. This paper explores the use of sentiment analysis tools on customer-support analyst conversations to find indicators of when a particular support ticket may be escalated. The results of this research indicate a considerable difference in the sentiment between escalated support tickets and non-escalated support tickets. Thus, this preliminary research provides us with the necessary information to further investigate how we can reliably predict support ticket escalations, and subsequently to provide insight to support analysts to better enable them to handle support tickets that may be escalated.
Denial-of-Service Attacks on C-V2X Networks
Venue: arXiv cs.CR 2020
Year: 2020

Cellular Vehicle-to-Everything (C-V2X) networks are increasingly adopted by automotive original equipment manufacturers (OEMs). C-V2X, as defined in 3GPP Release 14 Mode 4, allows vehicles to self-manage the network in absence of a cellular base-station. Since C-V2X networks convey safety-critical messages, it is crucial to assess their security posture. This work contributes a novel set of Denial-of-Service (DoS) attacks on C-V2X networks operating in Mode 4. The attacks are caused by adversarial resource block selection and vary in sophistication and efficiency. In particular, we consider "oblivious" adversaries that ignore recent transmission activity on resource blocks, "smart" adversaries that do monitor activity on each resource block, and "cooperative" adversaries that work together to ensure they attack different targets. We analyze and simulate these attacks to showcase their effectiveness. Assuming a fixed number of attackers, we show that at low vehicle density, smart and cooperative attacks can significantly impact network performance, while at high vehicle density, oblivious attacks are almost as effective as the more sophisticated attacks.
Interpretable Assessment of Fairness During Model Evaluation
Venue: arXiv cs.LG 2020
Year: 2020

For companies developing products or algorithms, it is important to understand the potential effects not only globally, but also on sub-populations of users. In particular, it is important to detect if there are certain groups of users that are impacted differently compared to others with regard to business metrics or for whom a model treats unequally along fairness concerns. In this paper, we introduce a novel hierarchical clustering algorithm to detect heterogeneity among users in given sets of sub-populations with respect to any specified notion of group similarity. We prove statistical guarantees about the output and provide interpretable results. We demonstrate the performance of the algorithm on real data from LinkedIn.
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

We present VisualHints, a novel environment for multimodal reinforcement learning (RL) involving text-based interactions along with visual hints (obtained from the environment). Real-life problems often demand that agents interact with the environment using both natural language information and visual perception towards solving a goal. However, most traditional RL environments either solve pure vision-based tasks like Atari games or video-based robotic manipulation; or entirely use natural language as a mode of interaction, like Text-based games and dialog systems. In this work, we aim to bridge this gap and unify these two approaches in a single environment for multimodal RL. We introduce an extension of the TextWorld cooking environment with the addition of visual clues interspersed throughout the environment. The goal is to force an RL agent to use both text and visual features to predict natural language action commands for solving the final task of cooking a meal. We enable variations and difficulties in our environment to emulate various interactive real-world scenarios. We present a baseline multimodal agent for solving such problems using CNN-based feature extraction from visual hints and LSTMs for textual feature extraction. We believe that our proposed visual-lingual environment will facilitate novel problem settings for the RL community.
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers
Venue: arXiv cs.CV 2020
Year: 2020

Liquid Chromatography coupled to Mass Spectrometry (LC-MS) based methods are commonly used for high-throughput, quantitative measurements of the proteome (i.e. the set of all proteins in a sample at a given time). Targeted LC-MS produces data in the form of a two-dimensional time series spectrum, with the mass to charge ratio of analytes (m/z) on one axis, and the retention time from the chromatography on the other. The elution of a peptide of interest produces highly specific patterns across multiple fragment ion traces (extracted ion chromatograms, or XICs). In this paper, we formulate this peak detection problem as a multivariate time series segmentation problem, and propose a novel approach based on the Transformer architecture. Here we augment Transformers, which are capable of capturing long distance dependencies with a global view, with Convolutional Neural Networks (CNNs), which can capture local context important to the task at hand, in the form of Transformers with Convolutional Self-Attention. We further train this model in a semisupervised manner by adapting state of the art semisupervised image classification techniques for multi-channel time series data. Experiments on a representative LC-MS dataset are benchmarked using manual annotations to showcase the encouraging performance of our method; it outperforms baseline neural network architectures and is competitive against the current state of the art in automated peak detection.
Enhancing road signs segmentation using photometric invariants
Venue: arXiv cs.CV 2020
Year: 2020

Road signs detection and recognition in natural scenes is one of the most important tasksin the design of Intelligent Transport Systems (ITS). However, illumination changes remain a major problem. In this paper, an efficient ap-proach of road signs segmentation based on photometric invariants is proposed. This method is based on color in-formation using a hybrid distance, by exploiting the chro-matic distance and the red and blue ratio, on l Theta Phi color space which is invariant to highlight, shading and shadow changes. A comparative study is performed to demonstrate the robustness of this approach over the most frequently used methods for road sign segmentation. The experimental results and the detailed analysis show the high performance of the algorithm described in this paper.
Multi-Class Zero-Shot Learning for Artistic Material Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Zero-Shot Learning (ZSL) is an extreme form of transfer learning, where no labelled examples of the data to be classified are provided during the training stage. Instead, ZSL uses additional information learned about the domain, and relies upon transfer learning algorithms to infer knowledge about the missing instances. ZSL approaches are an attractive solution for sparse datasets. Here we outline a model to identify the materials with which a work of art was created, by learning the relationship between English descriptions of the subject of a piece and its composite materials. After experimenting with a range of hyper-parameters, we produce a model which is capable of correctly identifying the materials used on pieces from an entirely distinct museum dataset. This model returned a classification accuracy of 48.42% on 5,000 artworks taken from the Tate collection, which is distinct from the Rijksmuseum network used to create and train our model.
A State-of-the-Art Review on IoT botnet Attack Detection
Venue: arXiv cs.NI 2020
Year: 2020

The Internet as we know it Today, comprises several fundamental interrelated networks, among which is the Internet of Things (IoT). Despite their versatility, several IoT devices are vulnerable from a security perspective, which renders them as a favorable target for multiple security breaches, especially botnet attacks. In this study, the conceptual frameworks of IoT botnet attacks will be explored, alongside several machinelearning based botnet detection techniques. This study also analyzes and contrasts several botnet Detection techniques based on the Bot-IoT Dataset; a recent realistic IoT dataset that comprises state-of-the-art IoT botnet attack scenarios.
How We Refactor and How We Document it? On the Use of Supervised Machine Learning Algorithms to Classify Refactoring Documentation
Venue: arXiv cs.SE 2020
Year: 2020

Refactoring is the art of improving the design of a system without altering its external behavior. Refactoring has become a well established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactorings in other development activities that go beyond improving the design. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. To cope with the above-mentioned limitations, we aim to better understand what motivates developers to apply refactoring by mining and classifying a large set of 111,884 commits containing refactorings, extracted from 800 Java projects. We trained a multi-class classifier to categorize these commits into 3 categories, namely, Internal QA, External QA, and Code Smell Resolution, along with the traditional BugFix and Functional categories. This classification challenges the original definition of refactoring, being exclusive to improving the design and fixing code smells. Further, to better understand our classification results, we analyzed commit messages to extract textual patterns that developers regularly use to describe their refactorings. The results show that (1) fixing code smells is not the main driver for developers to refactoring their codebases. Refactoring is solicited for a wide variety of reasons, going beyond its traditional definition; (2) the distribution of refactorings differs between production and test files; (3) developers use several patterns to purposefully target refactoring; (4) the textual patterns, extracted from commit messages, provide better coverage for how developers document their refactorings.
An investigation of Modern Foreign Language (MFL) teachers and their cognitions of Computer Assisted Language Learning (CALL) amid the COVID-19 health pandemic
Venue: arXiv cs.HC 2020
Year: 2020

A study was performed with 33 Modern Foreign Language (MFL) teachers to afford insight into how classroom practitioners interact with Computer Assisted Language Learning (CALL) in Second Language (L2) pedagogy. A questionnaire with CALL specific statements was completed by MFL teachers who were recruited via UK based Facebook groups. Significantly, participants acknowledged a gap in practice from the expectation of CALL in the MFL classroom. Overall, respondents were shown to be interested and regular consumers of CALL who perceived its ease and importance in L2 teaching and learning.
Precise Subtyping for Asynchronous Multiparty Sessions
Venue: arXiv cs.LO 2020
Year: 2020

This paper presents the first formalisation of the precise subtyping relation for asynchronous multiparty sessions. We show that our subtyping relation is sound (i.e., guarantees safe process replacement) and also complete: any extension of the relation is unsound. To achieve our results, we develop a novel session decomposition technique, from full session types (including internal/external choices) into single input/output session trees (without choices). Previous work studies precise subtyping for binary sessions (with just two participants), or multiparty sessions (with any number of participants) and synchronous interaction. Here, we cover multiparty sessions with asynchronous interaction, where messages are transmitted via FIFO queues (as in the TCP/IP protocol), and prove that our subtyping is both operationally and denotationally precise. In the asynchronous multiparty setting, finding the precise subtyping relation is a highly complex task: this is because, under some conditions, participants can permute the order of their inputs and outputs, by sending some messages earlier or receiving some later, without causing errors; the precise subtyping relation must capture all such valid permutations -- and consequently, its formalisation, reasoning and proofs become challenging. Our session decomposition technique overcomes this complexity, expressing the subtyping relation as a composition of refinement relations between single input/output trees, and providing a simple reasoning principle for asynchronous message optimisations.
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems
Venue: arXiv cs.LG 2020
Year: 2020

Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. While most of prior work has mainly focused on developing effective disease progression models using EHRs collected from an individual medical system, relatively little work has investigated building robust yet generalizable diagnosis models across different systems. In this work, we propose a general domain adaptation (DA) framework that tackles two categories of discrepancies in EHRs collected from different medical systems: one is caused by heterogeneous patient populations (covariate shift) and the other is caused by variations in data collection procedures (systematic bias). Prior research in DA has mainly focused on addressing covariate shift but not systematic bias. In this work, we propose an adversarial domain separation framework that addresses both categories of discrepancies by maintaining one globally-shared invariant latent representation across all systems} through an adversarial learning process, while also allocating a domain-specific model for each system to extract local latent representations that cannot and should not be unified across systems. Moreover, our proposed framework is based on variational recurrent neural network (VRNN) because of its ability to capture complex temporal dependencies and handling missing values in time-series data. We evaluate our framework for early diagnosis of an extremely challenging condition, septic shock, using two real-world EHRs from distinct medical systems in the U.S. The results show that by separating globally-shared from domain-specific representations, our framework significantly improves septic shock early prediction performance in both EHRs and outperforms the current state-of-the-art DA models.
FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries
Venue: arXiv cs.LG 2020
Year: 2020

Transfer learning is a useful machine learning framework that allows one to build task-specific models (student models) without significantly incurring training costs using a single powerful model (teacher model) pre-trained with a large amount of data. The teacher model may contain private data, or interact with private inputs. We investigate if one can leak or infer such private information without interacting with the teacher model directly. We describe such inference attacks in the context of face recognition, an application of transfer learning that is highly sensitive to personal privacy. Under black-box and realistic settings, we show that existing inference techniques are ineffective, as interacting with individual training instances through the student models does not reveal information about the teacher. We then propose novel strategies to infer from aggregate-level information. Consequently, membership inference attacks on the teacher model are shown to be possible, even when the adversary has access only to the student models. We further demonstrate that sensitive attributes can be inferred, even in the case where the adversary has limited auxiliary information. Finally, defensive strategies are discussed and evaluated. Our extensive study indicates that information leakage is a real privacy threat to the transfer learning framework widely used in real-life situations.